def testSingleImage(
network: torch.nn.Module, image: Image, transforms
) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
Logger.log("Testing single image!", logger="main")
data: torch.Tensor = transforms(image)
data = util.putOnDevice(data)
Logger.log("Resizing image.")
data.resize_(1, 3, 224, 224)
if Config.useCuda():
network = network.cuda()
else:
network = network.cpu()
Logger.log("Triggering dropout layers.")
network.train()
outputs: typing.List[torch.Tensor] = []
for i in range(Config.getArgs().num_of_tests):
outputs.append(network(data))
outs: np.ndarray = np.zeros((len(outputs), 7))
for i in range(len(outputs)):
outs[i] = outputs[i].cpu().detach().numpy()
Logger.log("Getting uncertainty.")
certPosMat, certOriMat, _, _ = getUncertainty(
output=torch.from_numpy(outs), returnMatrix=True)
Logger.log("Getting actual output.", logger="main")
network.eval()
output: np.ndarray = network(data).detach().cpu().numpy()
return output[0], outs, certPosMat, certOriMat
def train(epoch):
model.train()
train_loss = 0
ax = 0
print(len(train_loader.dataset)," ",len(train_loader))
for batch_idx, (data,_) in enumerate(train_loader):
ax += 1
print(data.size())
data.resize_((args.batch_size, 1, 64, 96))
print(data.size())
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = loss_function(recon_batch, data, mu, logvar)
loss.backward()
train_loss += loss.item()
optimizer.step()
#if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item() / len(data)))
print('====> Epoch: {} Average loss: {:.4f}'.format(
epoch, train_loss / len(train_loader.dataset)))
def __getitem__(self, index):
fpath = os.path.join(self.wav_dir, self.df.fname[index])
y, sr = librosa.load(fpath, sr=self.sr)
if sr is None:
print('WARNING:', fpath)
sr = 44100
# ランダムクロップ
y = random_crop(y, int(self.max_length * sr))
# 特徴抽出
n_fft = int(self.window_size * sr)
hop_length = int(self.hop_size * sr)
if self.feature == 'mfcc':
feature = librosa.feature.mfcc(y=y,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
n_mfcc=self.n_feature)
elif self.feature == 'melgram':
feature = librosa.feature.melspectrogram(y,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
n_mels=self.n_feature)
else:
print('Invalid feature name: %s' % self.feature)
exit(1)
data = torch.from_numpy(feature).float()
s = data.size()
if self.model_type == 'alex2d' or self.model_type == 'resnet':
# Conv2dの場合は (channel, features, frames)
data.resize_(1, s[0], s[1])
elif self.model_type == 'alex1d' or self.model_type == 'lstm':
# Conv1dの場合は (features, frames)
data.resize_(s[0], s[1])
else:
print('Invalid conv type: %s' % self.model_type)
exit(1)
mean = data.mean()
std = data.std()
if std != 0:
data.add_(-mean)
data.div_(std)
if self.test:
# テストモードのときは正解ラベルがないのでデータだけ返す
return data
else:
# label
label = self.df.label_idx[index]
return data, label
def test(testloader, epoch, isVal):
model.eval()
test_loss = 0
correct = 0
all_labels = 0
all_preds = 0
hidden = model.init_hidden(eval_batch_size)
for i, (data, target) in enumerate(testloader, 0):
if data.size(0) < args.batchSize * args.bptt:
smaller_batch_size = data.size(0) // args.bptt
if smaller_batch_size == 0:
break
data = data[:smaller_batch_size * args.bptt]
hidden = model.init_hidden(smaller_batch_size)
data.resize_(args.bptt, smaller_batch_size, inputSize)
target = target[:(smaller_batch_size * args.bptt)]
else:
data.resize_(args.bptt, args.batchSize, inputSize)
input.data.resize_(data.size()).copy_(data)
label.data.resize_(target.size()).copy_(target)
output, hidden = model(input, hidden)
test_loss += criterion(output, label)
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(label.data).cpu().sum()
if not torch.is_tensor(all_labels):
all_labels = target
all_preds = output.data[:, 1]
else:
all_labels = torch.cat((all_labels, target), 0)
all_preds = torch.cat((all_preds, output.data[:, 1]), 0)
test_loss /= len(testloader)
auc = metrics.roc_auc_score(all_labels.cpu().numpy(),
all_preds.cpu().numpy())
if isVal:
print(
'\n[%d/%d] ||VAL|| Average loss: %.4f, Accuracy: %d / %d (%.1f) AUC : %.6f \n'
% (epoch, args.epochs, test_loss.data[0], correct,
len(testloader.dataset),
100. * correct / len(testloader.dataset), auc))
else:
print(
'\n[%d/%d] ||TEST|| Average loss: %.4f, Accuracy: %d / %d (%.1f) AUC : %.6f \n'
% (epoch, epochs, test_loss.data[0], correct,
len(testloader.dataset),
100. * correct / len(testloader.dataset), auc))
return test_loss.data[0]
def test(epoch):
model.eval()
test_loss = 0
with torch.no_grad():
for i, (data, _) in enumerate(test_loader):
data = data.to(device)
recon_batch, mu, logvar = model(data)
test_loss += loss_function(recon_batch, data, mu, logvar).item()
if i == 0:
n = min(data.size(0), 20)
data.resize_((args.batch_size, 1, 64, 96))
comparison = torch.cat([data[:n], recon_batch.view(args.batch_size, 1, 64, 96)[:n]])
save_image(comparison.cpu(), '../bin/results/reconstruction_' + str(epoch) + '.png', nrow=n)
test_loss /= len(test_loader.dataset)
print('====> Test set loss: {:.4f}'.format(test_loss))
def train(trainloader, epoch):
model.train()
hidden = model.init_hidden(args.batchSize)
for i, (data, target) in enumerate(trainloader, 0):
if data.size(0) < args.batchSize * args.bptt:
smaller_batch_size = data.size(0) // args.bptt
if smaller_batch_size == 0:
break
data = data[:smaller_batch_size * args.bptt]
hidden = model.init_hidden(smaller_batch_size)
data.resize_(args.bptt, smaller_batch_size, inputSize)
target = target[:(smaller_batch_size * args.bptt)]
else:
data.resize_(args.bptt, args.batchSize, inputSize)
input.data.resize_(data.size()).copy_(data)
if args.noise > 0:
noise = torch.FloatTensor(data.size()).normal_(0, args.noise)
if args.cuda:
noise = noise.cuda()
input.data.add_(noise)
label.data.resize_(target.size()).copy_(target)
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(input, hidden)
loss = criterion(output, label)
loss.backward()
if args.optimizer == 'base':
clipped_lr = lr * clip_gradient(model, args.clip)
for p in model.parameters():
p.data.add_(-clipped_lr, p.grad.data)
elif args.optimizer in ['adam', 'RMSprop']:
clip_grad_norm(model.parameters(), args.clip)
optimizer.step()
if i % log_interval == 0:
print('[%d/%d] [%d/%d] Train Loss : %.4f' %
(epoch, args.epochs, i, len(trainloader), loss.data[0]))
def prepareData(train_loader, seq_length, batch_index):
index_start = batch_index * seq_length
index_end = index_start + seq_length
for i, (data, _) in enumerate(train_loader):
data = data.view(-1, input_dim)
data = data.resize_((batch_size, input_dim, 1))
if i == index_start:
outData = data
elif i > index_start and i < index_end:
outData = torch.cat((outData, data), 2)
return outData.permute(2, 0, 1)